U.S. patent application number 14/066400 was filed with the patent office on 2015-04-30 for data lifting for stop payment requests.
This patent application is currently assigned to BANK OF AMERICA CORPORATION. The applicant listed for this patent is BANK OF AMERICA CORPORATION. Invention is credited to Saravana Kumar Govindarajan, Brian David Hanson, Michael Scott Hjellming, Scott Andrew Johnson, Hyunmo Koo, Michael Gerald Smith.
Application Number | 20150120548 14/066400 |
Document ID | / |
Family ID | 52996532 |
Filed Date | 2015-04-30 |
United States Patent
Application |
20150120548 |
Kind Code |
A1 |
Smith; Michael Gerald ; et
al. |
April 30, 2015 |
DATA LIFTING FOR STOP PAYMENT REQUESTS
Abstract
Embodiments of the invention include systems, methods, and
computer-program products for lifting metadata from financial
documents to allow for automated financial document stop payment
requests. As such, allowing for automated determining of an
appropriate financial document to stop payment on based on user
information comparison to stored metadata for financial documents.
As such, the user may provide information about the requested stop
payment, such as a payee, date, amount, or the like associated with
a payment. The system may then utilize the user provided
information and compare that information to the metadata lifted
from the financial documents. As such, identifying the financial
document associated with the stop payment request. Once identified,
the system will stop the payment associated with that financial
document.
Inventors: |
Smith; Michael Gerald; (Fort
Mill, SC) ; Johnson; Scott Andrew; (Atlanta, GA)
; Hjellming; Michael Scott; (Cherryville, NC) ;
Hanson; Brian David; (Charlotte, NC) ; Govindarajan;
Saravana Kumar; (Atlanta, GA) ; Koo; Hyunmo;
(Atlanta, GA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
BANK OF AMERICA CORPORATION |
Charlotte |
NC |
US |
|
|
Assignee: |
BANK OF AMERICA CORPORATION
Charlotte
NC
|
Family ID: |
52996532 |
Appl. No.: |
14/066400 |
Filed: |
October 29, 2013 |
Current U.S.
Class: |
705/44 |
Current CPC
Class: |
G06Q 20/3276 20130101;
G06Q 20/407 20130101; G06Q 20/042 20130101 |
Class at
Publication: |
705/44 |
International
Class: |
G06Q 20/40 20060101
G06Q020/40 |
Claims
1. A system for stop payment request processing of financial
documents, the system comprising: a memory device with
computer-readable program code stored thereon; a communication
device; a processing device operatively coupled to the memory
device and the communication device, wherein the processing device
is configured to execute the computer-readable program code to:
receive financial documents associated with a user transaction for
processing, wherein the financial document includes a payment for
the user transaction; identify elements from the financial
document, wherein the elements from the financial document are
extracted from the financial document using optical character
recognition and the elements are stored as metadata; receive an
indication from a user requesting a stop payment of the financial
document, wherein the indication includes information about the
financial document to be stopped; compare the information about the
financial document to be stopped to the extracted metadata from the
received financial documents; match an appropriate financial
document to initiate a stop payment process, wherein the match of
an appropriate financial document is based on the comparison
between the information about the financial document to be stopped
to the extracted metadata from the received financial documents;
and issue a stop payment for the appropriate financial document,
such that the appropriate financial document is prohibited from
being processed.
2. The system of claim 1, wherein receiving an indication from a
user requesting a stop payment of the financial document further
comprises receiving information about the financial document to be
stopped, wherein the information includes one or more of a payor
name, a payee name, date, payment amount, account number, or
routing number.
3. The system of claim 1 further comprising processing the
financial documents that are not matched with the information about
the financial document to be stopped, wherein processing comprises
directing a financial document without an issued stop payment to an
appropriate financial account associated with the financial
document such that payment for the user transaction associated with
the financial document is completed.
4. The system of claim 3 further comprising identifying financial
documents without an issued stop payment that do not match to an
appropriate financial account, wherein the identified financial
documents are flagged for stop payment.
5. The system of claim 1, wherein matching an appropriate financial
document to initiate a stop payment process based on the comparison
between the information about the financial document to be stopped
to the extracted metadata from the received financial documents
further comprises identifying a confidence rating that the match is
correct prior to issuing a stop payment for the financial
document.
6. The system of claim 1, wherein comparing the information about
the financial document to be stopped to the extracted metadata from
the received financial documents further comprises: compiling
metadata associated with the financial documents that include a
payment for the user transaction; compiling information from the
user, wherein the information includes one or more of a payor name,
a payee name, date, payment amount, account number, or routing
number; compare the information from the user to the compiled
metadata associated with financial documents; and determine a match
between the compiled metadata associated with financial documents
and the information provided by the user, based on the comparison
of the information from the user to the compiled metadata.
7. The system of claim 1, wherein identifying elements from the
transaction documents associated with the user transaction further
comprises capturing via optical character recognition, images of
various elements of the transaction documents, wherein the various
elements are unique to the transaction documents, wherein the
identified data is stored as metadata.
8. The system of claim 1, wherein elements of the financial
document that comprise the identified data includes one or more of
a payor name, a payee name, date, payment amount, account number,
or routing number.
9. The system of claim 1, wherein metadata comprises both
structural and descriptive metadata, wherein the structural and
descriptive metadata includes design, specification, and details
from the financial document relating to one or more of account
data, dates, payee, payor, addresses, routing numbers, or payment
amounts.
10. A computer program product for stop payment request processing
of financial documents, the computer program product comprising at
least one non-transitory computer-readable medium having
computer-readable program code portions embodied therein, the
computer-readable program code portions comprising: an executable
portion configured for receiving financial documents associated
with a user transaction for processing, wherein the financial
document includes a payment for the user transaction; an executable
portion configured for identifying elements from the financial
document, wherein the elements from the financial document are
extracted from the financial document using optical character
recognition and the elements are stored as metadata; an executable
portion configured for receiving an indication from a user
requesting a stop payment of the financial document, wherein the
indication includes information about the financial document to be
stopped; an executable portion configured for comparing the
information about the financial document to be stopped to the
extracted metadata from the received financial documents; an
executable portion configured for matching an appropriate financial
document to initiate a stop payment process, wherein the match of
an appropriate financial document is based on the comparison
between the information about the financial document to be stopped
to the extracted metadata from the received financial documents;
and an executable portion configured for issuing a stop payment for
the appropriate financial document, such that the appropriate
financial document is prohibited from being processed.
11. The computer program product of claim 10, wherein receiving an
indication from a user requesting a stop payment of the financial
document further comprises receiving information about the
financial document to be stopped, wherein the information includes
one or more of a payor name, a payee name, date, payment amount,
account number, or routing number.
12. The computer program product of claim 10 further comprising an
executable portion configured for processing the financial
documents that are not matched with the information about the
financial document to be stopped, wherein processing comprises
directing a financial document without an issued stop payment to an
appropriate financial account associated with the financial
document such that payment for the user transaction associated with
the financial document is completed.
13. The computer program product of claim 10 further comprising an
executable portion configured for identifying financial documents
without an issued stop payment that do not match to an appropriate
financial account, wherein the identified financial documents are
flagged for stop payment.
14. The computer program product of claim 10, wherein matching an
appropriate financial document to initiate a stop payment process
based on the comparison between the information about the financial
document to be stopped to the extracted metadata from the received
financial documents further comprises identifying a confidence
rating that the match is correct prior to issuing a stop payment
for the financial document.
15. The computer program product of claim 10, wherein identifying
elements from the transaction documents associated with the user
transaction further comprises capturing via optical character
recognition, images of various elements of the transaction
documents, wherein the various elements are unique to the
transaction documents, wherein the identified data is stored as
metadata.
16. The computer program product of claim 10, wherein metadata
comprises both structural and descriptive metadata, wherein the
structural and descriptive metadata includes design, specification,
and details from the financial document relating to one or more of
account data, dates, payee, payor, addresses, routing numbers, or
payment amounts.
17. A computer-implemented method for stop payment request
processing of financial documents, the method comprising: providing
a computing system comprising a computer processing device and a
non-transitory computer readable medium, where the computer
readable medium comprises configured computer program instruction
code, such that when said instruction code is operated by said
computer processing device, said computer processing device
performs the following operations: receiving financial documents
associated with a user transaction for processing, wherein the
financial document includes a payment for the user transaction;
identifying elements from the financial document, wherein the
elements from the financial document are extracted from the
financial document using optical character recognition and the
elements are stored as metadata; receiving an indication from a
user requesting a stop payment of the financial document, wherein
the indication includes information about the financial document to
be stopped; comparing, via a computer processing device, the
information about the financial document to be stopped to the
extracted metadata from the received financial documents; matching
an appropriate financial document to initiate a stop payment
process, wherein the match of an appropriate financial document is
based on the comparison between the information about the financial
document to be stopped to the extracted metadata from the received
financial documents; and issuing a stop payment for the appropriate
financial document, such that the appropriate financial document is
prohibited from being processed.
18. The computer-implemented method of claim 17, wherein receiving
an indication from a user requesting a stop payment of the
financial document further comprises receiving information about
the financial document to be stopped, wherein the information
includes one or more of a payor name, a payee name, date, payment
amount, account number, or routing number.
19. The computer-implemented method of claim 17 further comprising
processing the financial documents that are not matched with the
information about the financial document to be stopped, wherein
processing comprises directing a financial document without an
issued stop payment to an appropriate financial account associated
with the financial document such that payment for the user
transaction associated with the financial document is
completed.
20. The computer-implemented method of claim 17 further comprising
identifying financial documents without an issued stop payment that
do not match to an appropriate financial account, wherein the
identified financial documents are flagged for stop payment.
21. The computer-implemented method of claim 17, wherein matching
an appropriate financial document to initiate a stop payment
process based on the comparison between the information about the
financial document to be stopped to the extracted metadata from the
received financial documents further comprises identifying a
confidence rating that the match is correct prior to issuing a stop
payment for the financial document.
22. The computer-implemented method of claim 17, wherein
identifying elements from the transaction documents associated with
the user transaction further comprises capturing via optical
character recognition, images of various elements of the
transaction documents, wherein the various elements are unique to
the transaction documents, wherein the identified data is stored as
metadata.
23. The computer-implemented method of claim 17, wherein metadata
comprises both structural and descriptive metadata, wherein the
structural and descriptive metadata includes design, specification,
and details from the financial document relating to one or more of
account data, dates, payee, payor, addresses, routing numbers, or
payment amounts.
Description
BACKGROUND
[0001] With advances in technology, entities and individuals alike
are starting to store more and more documents, pictures,
illustrations, or other images, electronically. In this way, the
space required for paper storage is drastically reduced and image
data is being stored on computers or databases.
[0002] Entities typically receive large volumes of documents from
vendors, customers, or employees on any given day. Each document,
especially if it is a financial document, is typically reconciled
with an account. In this way, specific characteristics of a
document are matched to a corresponding account.
[0003] However, sometimes there is no match made between the
document and a corresponding account. As such, when an exception
occurs, an individual may have to look to other characteristics of
the document for reconciliation purposes.
BRIEF SUMMARY
[0004] Embodiments of the present invention address the above needs
and/or achieve other advantages by providing apparatuses (e.g., a
system, computer program product and/or other devices) and methods
for lifting metadata off of documents to allow for automated
exception processing. As such, allowing for automated decisions for
exception processing to systematically resolve exceptions. The
exceptions may include one or more irregularities such as bad micro
line reads, outdated check stork, or misrepresentative checks that
may result in a failure to match the check to an associated account
for processing. In some embodiments, the metadata may be used for
automated payment stops in response to detecting a suspect document
or time. In yet other embodiments, the metadata may be used for
automated decisions for detecting and/or eliminating duplicate
check processing.
[0005] In some embodiments, the system may receive images of
financial documents from one or more sources. The financial
documents may be received from within an entity, from other
financial institutions, or the like. In some embodiments, the
images include images of checks or other financial documents
captured by an account holder or other entity. From the received
financial documents, the system may detect data from the financial
record image. This information may be any written or printed
information on the front or back of the financial document. The
documents may include a myriad of financial documents, including
but not limited to checks, lease documents, mortgage documents,
deposit slips, payment coupons, receipts, general ledger tickets,
or the like.
[0006] In the present invention, once the financial document is
received, the invention may extract and process the document or the
image of the document as metadata. In some embodiments, the system
may extract data, in the form of metadata from a text document. The
document, which may be a check or the like, may be utilized to
extract and/or collect the information associated with the document
into metadata instead of image or text data. The invention may then
utilize the metadata to further process the received document. The
metadata may include information such as an account data, dates,
payee, payor, addresses, routing numbers, amounts, document
backgrounds, or other information that may be imperative to
processing that document. The system may then store the data
collected from the document.
[0007] In some embodiments, the data collected from the document
may be processed and stored as metadata associated with the
document. In this way, the image of the document may be captured
and the data reprocessed into text or non-image data for storage.
As such, numbers, letters, or the like on the document may be
captured as part of the document image, but be stored as text
data.
[0008] In some embodiments, the system may extract the data from
financial document images or other image data. This data may be
lifted off of the financial documents and extracted as metadata.
Metadata is data about the image data found on a financial
document, such as a check, or the like. In some embodiments, the
data may be structural metadata. As such, the data may be about the
design and specification of the structure of the data. In other
embodiments, the data may be descriptive metadata. As such, the
data may be data describing in detail the content of the financial
record or document. In some embodiments, the metadata as described
herein may take the form of structural, descriptive and/or a
combination thereof.
[0009] In order to extract the metadata from one or more documents
or images optical character recognition may be utilized. In this
way, optical character recognition may be used to extract the
metadata from financial documents, such as text documents and
financial record images, such as checks or other financial
instruments.
[0010] In some embodiments, the metadata extracted from the
financial documents can be used in processing or automating
transactions, implementing business strategies, and providing
enhanced online account information to customers.
[0011] Specifically, in some embodiments the extracted metadata is
utilized to allow for automated decisions for exception processing
to systematically resolve exceptions. The exceptions may include
one or more irregularities such as bad micro line reads, outdated
check stork, or misrepresentative checks that may result in a
failure to match the check to an associated account for processing.
As such, once an exception is identified during the processing the
metadata lifted from the document with the exception may be
utilized to search financial records at the financial institution
to attempt to identify the correct version of the document.
Subsequently, the system may correct the irregularity
systematically and automatically.
[0012] In other some embodiments, the extracted metadata may be
used for automated payment stops in response to detecting a suspect
document or time. As such, the system may receive information about
a document that a user may wish to stop a payment on. As such, the
system may use this information and match it to metadata lifted off
of financial documents. The information received will match up to
metadata lifted from the financial document. As such, the system
will put a stop payment on the financial document identified.
[0013] In yet other embodiments, the metadata may be used for
automated decisions for detecting and/or eliminating duplicate
check processing. In this way, the system will lift metadata
associated with a financial document received. The metadata may
then be compared to metadata from previous financial documents
received. If there exists an exact match between the two sets of
metadata the invention will notify the user and identify the
duplicate financial document. Furthermore, the system will
eliminate the duplicate if necessary.
[0014] Embodiments of the invention relate to systems, methods, and
computer program products for stop payment request processing of
financial documents, the invention comprising: receiving financial
documents associated with a user transaction for processing,
wherein the financial document includes a payment for the user
transaction; identifying elements from the financial document,
wherein the elements from the financial document are extracted from
the financial document using optical character recognition and the
elements are stored as metadata; receiving an indication from a
user requesting a stop payment of the financial document, wherein
the indication includes information about the financial document to
be stopped; comparing the information about the financial document
to be stopped to the extracted metadata from the received financial
documents; matching an appropriate financial document to initiate a
stop payment process, wherein the match of an appropriate financial
document is based on the comparison between the information about
the financial document to be stopped to the extracted metadata from
the received financial documents; and issuing a stop payment for
the appropriate financial document, such that the appropriate
financial document is prohibited from being processed.
[0015] In some embodiments, receiving an indication from a user
requesting a stop payment of the financial document further
comprises receiving information about the financial document to be
stopped, wherein the information includes one or more of a payor
name, a payee name, date, payment amount, account number, or
routing number.
[0016] In some embodiments, the invention further comprises
processing the financial documents that are not matched with the
information about the financial document to be stopped, wherein
processing comprises directing a financial document without an
issued stop payment to an appropriate financial account associated
with the financial document such that payment for the user
transaction associated with the financial document is
completed.
[0017] In some embodiments, the invention further comprises
identifying financial documents without an issued stop payment that
do not match to an appropriate financial account, wherein the
identified financial documents are flagged for stop payment.
[0018] In some embodiments, matching an appropriate financial
document to initiate a stop payment process based on the comparison
between the information about the financial document to be stopped
to the extracted metadata from the received financial documents
further comprises identifying a confidence rating that the match is
correct prior to issuing a stop payment for the financial
document.
[0019] In some embodiments, comparing the information about the
financial document to be stopped to the extracted metadata from the
received financial documents further comprises: compiling metadata
associated with the financial documents that include a payment for
the user transaction; compiling information from the user, wherein
the information includes one or more of a payor name, a payee name,
date, payment amount, account number, or routing number; compare
the information from the user to the compiled metadata associated
with financial documents; and determine a match between the
compiled metadata associated with financial documents and the
information provided by the user, based on the comparison of the
information from the user to the compiled metadata.
[0020] In some embodiments, identifying elements from the
transaction documents associated with the user transaction further
comprises capturing via optical character recognition, images of
various elements of the transaction documents, wherein the various
elements are unique to the transaction documents, wherein the
identified data is stored as metadata.
[0021] In some embodiments, elements of the financial document that
comprise the identified data includes one or more of a payor name,
a payee name, date, payment amount, account number, or routing
number.
[0022] In some embodiments, metadata comprises both structural and
descriptive metadata, wherein the structural and descriptive
metadata includes design, specification, and details from the
financial document relating to one or more of account data, dates,
payee, payor, addresses, routing numbers, or payment amounts.
[0023] The features, functions, and advantages that have been
discussed may be achieved independently in various embodiments of
the present invention or may be combined with yet other
embodiments, further details of which can be seen with reference to
the following description and drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Having thus described embodiments of the invention in
general terms, reference will now be made to the accompanying
drawings, wherein:
[0025] FIG. 1A provides a high level process flow illustrating
general data lifting for image document exception processing, in
accordance with one embodiment of the present invention;
[0026] FIG. 1B provides a high level process flow illustrating
general data lifting for image document exception processing, in
accordance with one embodiment of the present invention;
[0027] FIG. 2 provides a high level process flow illustrating
identifying and extracting financial record data as metadata, in
accordance with one embodiment of the present invention;
[0028] FIG. 3 provides a data lifting for image document exception
processing system environment, in accordance with one embodiment of
the present invention;
[0029] FIG. 4 illustrates an exemplary image of a financial record,
in accordance with one embodiment of the present invention;
[0030] FIG. 5 provides an exemplary template of a financial record,
in accordance with one embodiment of the present invention;
[0031] FIG. 6 provides a process flow illustrating metadata lift
and utilization for exception processing, in accordance with one
embodiment of the present invention;
[0032] FIG. 7 provides a process flow illustrating metadata lift
and utilization for stop payment processing, in accordance with one
embodiment of the present invention; and
[0033] FIG. 8 provides a process flow illustrating metadata lift
and utilization for duplicate identification and processing, in
accordance with one embodiment of the present invention.
DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION
[0034] Embodiments of the present invention will now be described
more fully hereinafter with reference to the accompanying drawings,
in which some, but not all, embodiments of the invention are shown.
Indeed, the invention may be embodied in many different forms and
should not be construed as limited to the embodiments set forth
herein; rather, these embodiments are provided so that this
disclosure will satisfy applicable legal requirements. Like numbers
refer to elements throughout. Where possible, any terms expressed
in the singular form herein are meant to also include the plural
form and vice versa, unless explicitly stated otherwise. As used
herein, a "document" or "financial record" may also refer to a
myriad of financial documents, including but not limited to a lease
document, a mortgage document, a deposit slip, a payment coupon, a
receipt, general ledger tickets, or the like. In some embodiments,
"document" or "financial record" may exist as a physical item
printed on paper or other medium. In other embodiments, the check
may exist electronically. Furthermore, "document" or "financial
record" may also refer to records associated with government data,
legal data, identification data, and the like. Although the
disclosure is directed to financial records, it will be understood
that non-financial records such as social communications,
advertising, blogs, opinion writing, and the like may also be
applicable to the disclosure presented herein. In cases were
non-financial records are use, it will be understood that personal
information, such personal identifying information, account
numbers, and the like, can be removed from the documents before
they are released. For example, if a coupon or product review is to
be used in advertising, personal information associated with such
records will be removed before the advertising is presented to the
public. The data of the financial records or non-financial records
may be provided in a wide variety formats including, paper records,
electronic or digital records, video records, audio records, and/or
combinations thereof. In some embodiments, the "document" or
"financial record" may be referred to in examples as a check or the
like.
[0035] Furthermore, the term "image lift data" or "data lift" may
refer to the process of lifting one or more areas/elements of a
document and storing those areas as metadata without storing the
entire document as an image file.
[0036] Some portions of this disclosure are written in terms of a
financial institution's unique position with respect document
processing and retrieving. As such, a financial institution may be
able to utilize its unique position to receive, store, process, and
retrieve images of documents, such as those of a financial
nature.
[0037] As presented herein, embodiments that detect and extract
specific data from images and that analyze, process, and distribute
extracted metadata are provided.
[0038] Referring now to FIG. 1A, FIG. 1A presents provides a high
level process flow for general data lifting for image document
exception processing 150, in accordance with some embodiments of
the invention. At block 120, the method comprises receiving an
image of a check. The image received may be one or more of a check
or other document or financial record. In some embodiments, the
image of the check may be received by an apparatus (e.g. a computer
system) via a user's mobile device, a camera, an Automated Teller
Machine (ATM) at one of the entity's facilities, a second apparatus
at a teller's station, or the like. In other embodiments, the
apparatus may be configured to capture the image of the check.
[0039] As illustrated in block 122, the system may then lift data
off of the check(document or financial record) using optical
character recognition (OCR). The OCR processes enables the system
to convert text and other symbols in the check images to other
formats such as text files and/or metadata, which can then be used
and incorporated into a variety of applications, documents, and
processes. In some embodiments, OCR based algorithms used in the
OCR processes incorporate pattern matching techniques. For example,
each character in an imaged word, phrase, code, or string of
alphanumeric text can be evaluated on a pixel-by-pixel basis and
matched to a stored character. Various algorithms may be repeatedly
applied to determine the best match between the image and stored
characters.
[0040] After the successful retrieval or capture of the image of
the check, the apparatus may process the image of the check as
illustrated in block 126. The apparatus may capture individual
pieces of check information from the image of the check in metadata
form. In some embodiments, the check information may be text. In
other embodiments, the check information may be an image processed
into a metadata format.
[0041] As illustrated in block 124, the method comprises storing
the check information in a metadata form. After the image of the
check is processed, the apparatus may store the lifted and
collected check information as metadata. In some embodiments, the
check information may be stored as metadata. As such, individual
elements of the check information may be stored separately, and may
be associated with each other via metadata. In some embodiments,
the individual pieces of check information may be stored together.
In some embodiments, the apparatus may additionally store the
original image of the check immediately after the image of the
check is received.
[0042] As illustrated in block 128, the process 150 continues by
identifying exceptions in the document processing. Exceptions may
be one or more of irregularities such as bad micro line reads,
outdated document stock, misrepresented items, or the like that
result in a failure to match the document to an account. In some
embodiments, the process may also detect duplicate documents. In
yet other embodiments, the system may identify payment stops for
specific documents.
[0043] Next, as illustrated in block 130, the process 150 continues
to provide automated decisions for exception processing utilizing
the lifted metadata. In this way, the system may utilize the
metadata lifted from the document in order to rectify the exception
identified in block 128. In this way, the system may be able to
rectify the exception without having to have an individual manually
override the exception and identify the account associated with the
document with the exception.
[0044] Referring now to FIG. 1B, FIG. 1B presents provides a high
level process flow for general data lifting for image document
exception processing 160, in accordance with some embodiments of
the invention. As illustrated in block 132, the process 160 starts
by identifying the exceptions in financial document processing.
Once identified, the documents associated with each of the one or
more exceptions may be categorized as either debit or credit
documents, as illustrated in block 134. In this way, the system may
identify an exception and identify the type of document that the
exception was identified from.
[0045] Next, as illustrate in decision block 136, the system may
identify if the document is a check or if it is another financial
document for processing. If the financial document is a check in
decision block 136, the system will identify if the check is a
pre-authorized draft check. In some embodiments, pre-authorized
draft checks are made via online purchases that ask a user for
his/her check number and routing number. The pre-authorized draft
check is subsequently converted to paper form and submitted to the
financial institution for processing. These pre-authorized draft
checks may undergo a higher level of processing scrutiny to ensure
authenticity, if necessary.
[0046] Finally, as illustrated in block 140, automated decisions
are created for the financial documents with exceptions based on
lifted metadata and the type of exception identified.
[0047] Referring now to FIG. 2, FIG. 2 provides a flowchart
illustrating a process 100 for identifying and extracting data from
check images. One or more devices, such as the one or more systems
and/or one or more computing devices and/or servers of FIG. 3 can
be configured to perform one or more steps of the process 100 or
other processes described below. In some embodiments, the one or
more devices performing the steps are associated with a financial
institution. In other embodiments, the one or more devices
performing the steps are associated with a merchant, business,
partner, third party, credit agency, account holder, and/or
user.
[0048] As illustrated at block 102, one or more check images are
received. The check images comprise the front portion of a check,
the back portion of a check, or any other portions of a check. In
cases where there are several checks piled into a stack, the
multiple check images may include, for example, at least a portion
of each of the four sides of the check stack. In this way, any
text, numbers, or other data provided on any side of the check
stack may also be used in implementing the process 100.
[0049] In some embodiments, each of the check images comprises
financial record data. The financial record data includes dates
financial records are issued, terms of the financial record, time
period that the financial record is in effect, identification of
parties associated with the financial record, payee information,
payor information, obligations of parties to a contract, purchase
amount, loan amount, consideration for a contract, representations
and warranties, product return policies, product descriptions,
check numbers, document identifiers, account numbers, merchant
codes, file identifiers, source identifiers, and the like.
[0050] Although check images are illustrated in FIG. 2, it will be
understood that any type of financial record image may be received
in accordance with the embodiments of FIG. 2. Exemplary check
images include PDF files, scanned documents, digital photographs,
and the like. At least a portion of each of the check images, in
some embodiments, is received from a financial institution, a
merchant, a signatory of the financial record (e.g., the entity
having authority to endorse or issue a financial record), and/or a
party to a financial record. In other embodiments, the check images
are received from image owners, account holders, agents of account
holders, family members of account holders, financial institution
customers, payors, payees, third parties, and the like. In some
embodiments, the source of at least one of the checks includes an
authorized source such as an account holder or a third party
financial institution. In other embodiments, the source of at least
one of the checks includes an unauthorized source such as an entity
that intentionally or unintentionally deposits or provides a check
image to the system of process 100.
[0051] In some exemplary embodiments, a customer or other entity
takes a picture of a check at a point of sales or an automated
teller machine (ATM) and communicates the resulting check image to
a point of sales device or ATM via wireless technologies, near
field communication (NFC), radio frequency identification (RFID),
and other technologies. In other examples, the customer uploads or
otherwise sends the check image to the system of process 100 via
email, short messaging service (SMS) text, a web portal, online
account, mobile applications, and the like. For example, the
customer may upload a check image to deposit funds into an account
or pay a bill via a mobile banking application using a capture
device. The capture device can include any type or number of
devices for capturing images or converting a check to any type of
electronic format such as a camera, personal computer, laptop,
notebook, scanner, mobile device, and/or other device.
[0052] As illustrated at block 104, optical character recognition
(OCR) processes are applied to at least a portion of the check
images. At least one OCR process may be applied to each of the
check images or some of the check images. The OCR processes enables
the system to convert text and other symbols in the check images to
other formats such as text files and/or metadata, which can then be
used and incorporated into a variety of applications, documents,
and processes. In some embodiments, OCR based algorithms used in
the OCR processes incorporate pattern matching techniques. For
example, each character in an imaged word, phrase, code, or string
of alphanumeric text can be evaluated on a pixel-by-pixel basis and
matched to a stored character. Various algorithms may be repeatedly
applied to determine the best match between the image and stored
characters.
[0053] As illustrated in block 106, the check data may be
identified based on the applied OCR processing. In some
embodiments, the OCR process includes location fields for
determining the position of data on the check image. Based on the
position of the data, the system can identify the type of data in
the location fields to aid in character recognition. For example,
an OCR engine may determine that text identified in the upper right
portion of a check image corresponds to a check number. The
location fields can be defined using any number of techniques. In
some embodiments, the location fields are defined using heuristics.
The heuristics may be embodied in rules that are applied by the
system for determining approximate location.
[0054] In other embodiments, the system executing process flow 100
defines the location fields by separating the portions and/or
elements of the image of the check into quadrants. As referred to
herein, the term quadrant is used broadly to describe the process
of differentiating elements of a check image by separating portions
and/or elements of the image of the check into sectors in order to
define the location fields. These sectors may be identified using a
two-dimensional coordinate system or any other system that can be
used for determining the location of the sectors. In many
instances, each sector will be rectangular in shape. In some
embodiments, the system identifies each portion of the image of the
check using a plurality of quadrants. In such an embodiment, the
system may further analyze each quadrant using the OCR algorithms
in order to determine whether each quadrant has valuable or useful
information. Generally, valuable or useful information may relate
to any data or information that may be used for processing and/or
settlement of the check, used for identifying the check, and the
like. Once the system determines the quadrants of the image of the
check having valuable and/or useful information, the system can
extract the identified quadrants together with the information from
the image of the check for storage. The quadrants may be extracted
as metadata, text, or code representing the contents of the
quadrant. In some embodiments, the quadrants of the image of the
check that are not identified as having valuable and/or useful
information are not extracted from the image.
[0055] In additional embodiments, the system uses a grid system to
identify non-data and data elements of a check image. The grid
system may be similar to the quadrant system. Using the grid
system, the system identifies the position of each grid element
using a coordinate system (e.g., x and y coordinates or x, y, and z
coordinate system or the like) or similar system for identifying
the spatial location of a grid element on a check. In practice, the
spatial location of a grid element may be appended to or some
manner related to grid elements with check data. For example, using
the grid, the system may identify which grid elements of the grid
contain data elements, such as check amount and payee name, and
either at the time of image capture or extraction of the check
image within the grid, the system can tag the grid element having
the check data element with the grid element's spatial location. In
some embodiments, the grid system and/or quadrant system is based
on stock check templates obtained from check manufacturers or
merchants (See, e.g., FIG. 5).
[0056] In alternative or additional embodiments, the OCR process
includes predefined fields to identify data. The predefined field
includes one or more characters, words, or phrases that indicate a
type of data. In such embodiments, the system of process 100
extracts all the data presented in the check image regardless of
the location of the data and uses the predefined fields to aid in
character recognition. For example, a predefined field containing
the phrase "Pay to the order of" may be used to determine that data
following the predefined field relates to payee information.
[0057] In addition to OCR processes, the system of process 100 can
use other techniques such as image overlay to locate, identify, and
extract data from the check images. In other embodiments, the
system uses the magnetic ink character recognition (MICR) to
determine the position of non-data (e.g., white space) and data
elements on a check image. For example, the MICR of a check may
indicate to the system that the received or captured check image is
a business check with certain dimensions and also, detailing the
location of data elements, such as the check amount box or Payee
line. In such an instance, once the positions of this information
is made available to the system, the system will know to capture
any data elements to the right or to the left of the identified
locations or include the identified data element in the capture.
This system may choose to capture the data elements of a check in
any manner using the information determined from the MICR number of
the check.
[0058] As illustrated at block 108, unrecognized data from the
check images is detected. In some embodiments, the unrecognized
data includes characters, text, shading, or any other data not
identified by the OCR processes. In such embodiments, the
unrecognized data is detected following implementation of at least
one of the OCR processes. In other embodiments, the unrecognized
data is detected prior to application of the OCR processes. For
example, the unrecognized data may be removed and separated from
the check images or otherwise not subjected to the OCR processes.
In one exemplary situation, the system may determine that
handwritten portions of a check image should not undergo OCR
processing due to the difficulty in identifying such handwritten
portions. Exemplary unrecognized data includes handwritten text,
blurred text, faded text, misaligned text, misspelled data, any
data not recognized by the OCR processes or other data recognition
techniques, and the like. In other cases, at least a portion of
some or all of the check images may undergo pre-processing to
enhance or correct the unrecognized data. For example, if the text
of a check image is misaligned or blurry, the system may correct
that portion of the check image before applying the OCR processes
to increase the probability of successful text recognition in the
OCR processes or other image processes.
[0059] As illustrated at block 110, inputted information
identifying the unrecognized data from a customer and/or an
operator is received. In some embodiments, an operator is provided
with the portions of a check image corresponding to the
unrecognized data. The operator can view the unrecognized data to
translate the unrecognized data into text and input the translation
into a check data repository. In this way, the system "learns" to
recognize previously unrecognized data such that when the system
reviews the same or similar unrecognized data in the future, such
data can be easily identified by reference to the check data
repository. In other embodiments, the system may present an online
banking customer with the unrecognized data to solicit input
directly from the customer. For example, the customer may be
presented with operator-defined terms of previously unrecognized
data to verify if such terms are correct. The system may solicit
corrective input from the customer via an online banking portal, a
mobile banking application, and the like. If an operator initially
determines that the handwriting on the memo line reads "house
flaps," the customer may subsequently correct the operator's
definition and update the check data repository so that the
handwritten portion correctly corresponds to "mouse traps." In some
embodiments, the customer's input is stored in a customer input
repository, which is linked to the check data repository associated
with the OCR processes. For example, the system can create a file
path linking the customer input repository with the check data
repository to automatically update the check data repository with
the customer input. In other embodiments, the check data repository
and/or customer input repository includes stored customer data or
account data. Stored customer signatures, for example, may be
included in the check data repository and/or customer input
repository.
[0060] As illustrated at block 112, business strategies and
transactions are processed based on at least one of the check data
and the inputted information. Metadata extracted from the check
images using the process 100 may be used to automate or enhance
various processes such as remediating exception processes,
replacing check images with check data in online statements,
enforcing requirements regarding third party check deposits,
facilitating check to automated clearing house transaction
conversion, cross selling products, and so forth.
[0061] FIG. 3 illustrates a data lifting for image document
exception processing system environment 200, in accordance with
some embodiments of the invention. The environment 200 includes a
computing device 211 of a user 210 (e.g., an account holder, a
mobile application user, an image owner, a bank customer, and the
like), a third party system 260, and a financial institution system
240. In some embodiments, the third party system 260 corresponds to
a third party financial institution. The environment 200 further
includes one or more third party systems 292 (e.g., a partner,
agent, or contractor associated with a financial institution), one
or more other financial institution systems 294 (e.g., a credit
bureau, third party banks, and so forth), and one or more external
systems 296.
[0062] The systems and devices communicate with one another over
the network 230 and perform one or more of the various steps and/or
methods according to embodiments of the disclosure discussed
herein. The network 230 may include a local area network (LAN), a
wide area network (WAN), and/or a global area network (GAN). The
network 230 may provide for wireline, wireless, or a combination of
wireline and wireless communication between devices in the network.
In one embodiment, the network 230 includes the Internet.
[0063] The computing device 211, the third party system 260, and
the financial institution system 240 each includes a computer
system, server, multiple computer systems and/or servers or the
like. The financial institution system 240, in the embodiments
shown has a communication device 242 communicably coupled with a
processing device 244, which is also communicably coupled with a
memory device 246. The processing device 244 is configured to
control the communication device 242 such that the financial
institution system 240 communicates across the network 230 with one
or more other systems. The processing device 244 is also configured
to access the memory device 246 in order to read the computer
readable instructions 248, which in some embodiments includes a one
or more OCR engine applications 250 and a client keying application
251. The memory device 246 also includes a datastore 254 or
database for storing pieces of data that can be accessed by the
processing device 244. In some embodiments, the datastore 254
includes a check data repository.
[0064] As used herein, a "processing device," generally refers to a
device or combination of devices having circuitry used for
implementing the communication and/or logic functions of a
particular system. For example, a processing device may include a
digital signal processor device, a microprocessor device, and
various analog-to-digital converters, digital-to-analog converters,
and other support circuits and/or combinations of the foregoing.
Control and signal processing functions of the system are allocated
between these processing devices according to their respective
capabilities. The processing device 214, 244, or 264 may further
include functionality to operate one or more software programs
based on computer-executable program code thereof, which may be
stored in a memory. As the phrase is used herein, a processing
device 214, 244, or 264 may be "configured to" perform a certain
function in a variety of ways, including, for example, by having
one or more general-purpose circuits perform the function by
executing particular computer-executable program code embodied in
computer-readable medium, and/or by having one or more
application-specific circuits perform the function.
[0065] Furthermore, as used herein, a "memory device" generally
refers to a device or combination of devices that store one or more
forms of computer-readable media and/or computer-executable program
code/instructions. Computer-readable media is defined in greater
detail below. For example, in one embodiment, the memory device 246
includes any computer memory that provides an actual or virtual
space to temporarily or permanently store data and/or commands
provided to the processing device 244 when it carries out its
functions described herein.
[0066] The user's computing device 211 includes a communication
device 212 and an image capture device 215 (e.g., a camera)
communicably coupled with a processing device 214, which is also
communicably coupled with a memory device 216. The processing
device 214 is configured to control the communication device 212
such that the user's computing device 211 communicates across the
network 230 with one or more other systems. The processing device
214 is also configured to access the memory device 216 in order to
read the computer readable instructions 218, which in some
embodiments includes a capture application 220 and an online
banking application 221. The memory device 216 also includes a
datastore 222 or database for storing pieces of data that can be
accessed by the processing device 214.
[0067] The third party system 260 includes a communication device
262 and an image capture device (not shown) communicably coupled
with a processing device 264, which is also communicably coupled
with a memory device 266. The processing device 264 is configured
to control the communication device 262 such that the third party
system 260 communicates across the network 230 with one or more
other systems. The processing device 264 is also configured to
access the memory device 266 in order to read the computer readable
instructions 268, which in some embodiments includes a transaction
application 270. The memory device 266 also includes a datastore
272 or database for storing pieces of data that can be accessed by
the processing device 264.
[0068] In some embodiments, the capture application 220, the online
banking application 221, and the transaction application 270
interact with the OCR engines 250 to receive or provide financial
record images and data, detect and extract financial record data
from financial record images, analyze financial record data, and
implement business strategies, transactions, and processes. The OCR
engines 250 and the client keying application 251 may be a suite of
applications for conducting OCR.
[0069] In some embodiments, the capture application 220, the online
banking application 221, and the transaction application 270
interact with the OCR engines 250 to utilize the extracted metadata
to determine decisions for exception processing. In this way, the
system may systematically resolve exceptions. The exceptions may
include one or more irregularities such as bad micro line reads,
outdated check stork, or misrepresentative checks that may result
in a failure to match the check to an associated account for
processing. As such, the system may identify the exception and code
it for exception processing. Furthermore, the system may utilize
the metadata to match the check to a particular account
automatically.
[0070] In some embodiments, the capture application 220, the online
banking application 221, and the transaction application 270
interact with the OCR engines 250 to utilize the extracted metadata
for automated payment stops when detecting a suspect document or
time during processing. In this way, the system may identify
suspect items within the extracted metadata. The document or check
processing may be stopped because of this identification. In some
embodiments, the suspect items may be detected utilizing OCR based
on data received from a customer external to the document in
comparison to the document. In some embodiments, the suspect items
may be detected utilizing OCR based on data associated with the
account in comparison to the document.
[0071] In some embodiments, the capture application 220, the online
banking application 221, and the transaction application 270
interact with the OCR engines 250 to utilize the extracted metadata
for automated decisions for detecting and/or eliminating duplicate
check processing. Duplicate checks may be detected and/or
eliminated based on metadata matching. In this way, data may be
lifted off of a document as metadata and compare the data to other
documents utilizing the metadata form. As such, the system does not
have to overlay images in order to detect duplicate documents.
[0072] The applications 220, 221, 250, 251, and 270 are for
instructing the processing devices 214, 244 and 264 to perform
various steps of the methods discussed herein, and/or other steps
and/or similar steps. In various embodiments, one or more of the
applications 220, 221, 250, 251, and 270 are included in the
computer readable instructions stored in a memory device of one or
more systems or devices other than the systems 260 and 240 and the
user's computing device 211. For example, in some embodiments, the
application 220 is stored and configured for being accessed by a
processing device of one or more third party systems 292 connected
to the network 230. In various embodiments, the applications 220,
221, 250, 251, and 270 stored and executed by different
systems/devices are different. In some embodiments, the
applications 220, 221, 250, 251, and 270 stored and executed by
different systems may be similar and may be configured to
communicate with one another, and in some embodiments, the
applications 220, 221, 250, 251, and 270 may be considered to be
working together as a singular application despite being stored and
executed on different systems.
[0073] In various embodiments, one of the systems discussed above,
such as the financial institution system 240, is more than one
system and the various components of the system are not collocated,
and in various embodiments, there are multiple components
performing the functions indicated herein as a single device. For
example, in one embodiment, multiple processing devices perform the
functions of the processing device 244 of the financial institution
system 240 described herein. In various embodiments, the financial
institution system 240 includes one or more of the external systems
296 and/or any other system or component used in conjunction with
or to perform any of the method steps discussed herein. For
example, the financial institution system 240 may include a
financial institution system, a credit agency system, and the
like.
[0074] In various embodiments, the financial institution system
240, the third party system 260, and the user's computing device
211 and/or other systems may perform all or part of a one or more
method steps discussed above and/or other method steps in
association with the method steps discussed above. Furthermore,
some or all the systems/devices discussed here, in association with
other systems or without association with other systems, in
association with steps being performed manually or without steps
being performed manually, may perform one or more of the steps of
method 300, the other methods discussed below, or other methods,
processes or steps discussed herein or not discussed herein.
[0075] FIG. 4 provides an illustration of an exemplary image of a
financial record 300, in accordance with one embodiment of the
present invention. The financial record illustrated in FIG. 4 is a
check. However, one will appreciate that any financial record,
financial document, or the like may be provided.
[0076] The image of check 300 may comprise an image of the entire
check, a thumbnail version of the image of the check, individual
pieces of check information, all or some portion of the front of
the check, all or some portion of the back of the check, or the
like. Check 300 comprises check information, wherein the check
information comprises contact information 305, the payee 310, the
memo description 315, the account number and routing number 320
associated with the appropriate user or customer account, the date
325, the check number 330, the amount of the check 335, the
signature 340, or the like. In some embodiments, the check
information may comprise text. In other embodiments, the check
information may comprise an image. A capture device (e.g., the
user's computing device 212 of FIG. 3) may capture an image of the
check 300 and transmit the image to a system of a financial
institution (e.g., the financial institution system 240 of FIG. 3)
via a network. The system may collect the check information from
the image of the check 300 and store the check information in a
datastore as metadata (e.g., the datastore 254 of FIG. 3). In some
embodiments, the pieces of check information may be stored in the
datastore individually. In other embodiments, multiple pieces of
check information may be stored in the datastore together.
[0077] FIG. 5 illustrates an exemplary template of a financial
record 400, in accordance with one embodiment of the present
invention. Again, the financial record illustrated in FIG. 5 is a
check. However, one will appreciate that any financial record,
financial document, or the like may be provided.
[0078] In the illustrated embodiment, the check template 400
corresponds to the entire front portion of a check, but it will be
understood that the check template 400 may also correspond to
individual pieces of check information, portions of a check, or the
like. The check template, in some embodiments, includes the format
of certain types of checks associated with a bank, a merchant, an
account holder, types of checks, style of checks, check
manufacturer, and so forth. By using the check template, the system
of process 100 any other system can "learn" to map the key
attributes of the check for faster and more accurate processing. In
some embodiments, financial records are categorized by template.
The check template 400 is only an exemplary template for a
financial record, and other check templates or other financial
record templates may be utilized to categorize checks or other
financial records. The check template 400 can be used in the OCR
processes, image overlay techniques, and the like.
[0079] The check template 400 comprises check information, wherein
the check information includes, for example, a contact information
field 405, a payee line field 410, a memo description field 415, an
account number and routing number field 420 associated with the
appropriate user or customer account, a date line field 425, a
check number field 430, an amount box field 435, a signature line
field 440, or the like.
[0080] FIG. 6 illustrates a process flow for metadata lifting and
utilization for exception processing 500, in accordance with one
embodiment of the present invention. As illustrated in block 502
and described in more detail above with respects to FIGS. 1-5, the
process 500 is initiated when financial documents, such as checks,
are received. The received financial document may be in various
forms, such as in an image format. Processing of the document may
proceed wherein the data from the document may be collected and
lifted from the document as metadata. This metadata is lifted from
the document utilizing optical character recognition (OCR). The OCR
processes enables the system to convert text and other symbols in
the document image to metadata, which can then be used and
incorporated into exception processing. In some embodiments, OCR
based algorithms used in the OCR processes incorporate pattern
matching techniques. For example, each character in an imaged word,
phrase, code, or string of alphanumeric text can be evaluated on a
pixel-by-pixel basis and matched to a stored character. Various
algorithms may be repeatedly applied to determine the best match
between the image and stored characters.
[0081] Once the metadata is lifted from the document as illustrated
in block 502, the process 500 continues to compile and store the
metadata associated with the received financial documents, as
illustrated in block 504. As such, after the image of the document,
such as a check, is processed, the system may compile and store the
lifted and collected check information as metadata. As such,
individual elements of the check information may be stored
separately, together, or the like. In this way, the system stores
the type of document, the appearance of the document, the
information on the document, such as numbers, accounts, dates,
names, addresses, payee, payor, routing numbers, amounts, document
backgrounds, or the like as metadata.
[0082] In some embodiments, the stored data may be structural
metadata. As such, the data may be about the design and
specification of the structure of the data. In other embodiments,
the data may be descriptive metadata. As such, the data may be data
describing in detail the content of the financial record or
document. In some embodiments, the metadata as described herein may
take the form of structural, descriptive and/or a combination
thereof.
[0083] Next, as illustrated in decision block 506, the system
monitors the received documents to identify exceptions in the
document processing. Exceptions may be one or more of
irregularities such as bad micro line reads, outdated document
stock, misrepresented items, or the like that result in a failure
to match the document to an account intended to be associated with
that document. If no exception is identified, then the process 500
terminates.
[0084] As illustrated in block 507 the process 500 continues to
identify and categorize any identified exceptions into financial
documents associated with debits or financial documents associated
with credits. As illustrated in block 508 the process 500 continues
to confirm the irregularity in the financial document that lead to
the exception identification in decision block 506. The
irregularity that lead to the exception may be one or more of a bad
micro line read, outdated documents (such as an outdated check or
deposit statement), or a general failure of the document to match
an existing financial account.
[0085] Next, as illustrated in block 510, the process 500 continues
to utilize the metadata associated with the received financial
documents to systematically search for exception resolutions. As
such, providing automated decisions for exception processing
utilizing the lifted metadata. As such, the metadata lifted from
the financial documents may be utilized to search the accounts or
other records at the financial institution to determine the correct
account or record associated with the exception document. For
example, the exception may include an outdated check. In this way,
one or more of the routing numbers, account numbers, or the like
may be incorrectly stated on the check. The system will take the
data on that outdated check and convert it to a metadata format.
Thus, the system will utilize the metadata format of the routing
number or the like to search the financial institution records to
identify that that particular routing number was used for a batch
of checks for User 1. As such, the system will identify the correct
user, User 1 associated with the check that had an exception. Other
examples may include one or more of bad micro line reads, document
or check format issues, or the like.
[0086] As such, the system may utilize the metadata lifted from the
document in order to rectify the exception identified in decision
block 506. In this way, the system may be able to rectify the
exception without having to have an individual manually override
the exception and identify the account associated with the document
with the exception.
[0087] In some embodiments, the system may not be able to identify
the correct account associated with the document based on the
metadata searching of all financial institution records. As such,
as illustrated in block 518, no match is found between the
financial document with the exception and the financial institution
records. In some embodiments, this may be due to exceptions in the
reading process so great that the numbers or letters may not be
identified, even partially. In other embodiments, these documents
may have one or more altered numbers or letters on the document,
flagging it as not being able to be matched to a current financial
account at the financial institution. As such, no exception
resolution may be identified for that particular exception. Next,
as illustrated in block 520, the system may queue the document that
is not identified for exception resolution for further
investigation by the financial institution.
[0088] Referring back to block 512 of FIG. 6, if a match between
the financial document with the exception and a financial account
or other financial institution record may be made, then the system
continues and automatically and systematically corrects the
exception based on the match, as illustrated in block 514. In some
embodiments, there may be one or more threshold confidences related
to the exception. As such, if a match has been made between the
metadata and a financial account and it is above a pre-determined
confidence, then the system may automatically correct the
exception. However, in some embodiments, the system may request
manual acceptance of the correction of the exception.
[0089] Finally, as illustrated in block 516, the corrected
financial document may be placed back into the financial document
processing for continued processing after the exception has been
identified and corrected via systematic searching financial
institution data utilizing metadata extracted from the original
financial document with an exception.
[0090] FIG. 7 illustrates a process flow for metadata lift and
utilization for stop payment processing 600, in accordance with one
embodiment of the present invention. As illustrated in block 602,
the process 500 is initiated when financial documents associated
with a payment, such as checks deposits, or the like, are received.
As such, these documents are used by a user to transfer a payment
or receive a payment. The received financial document associated
with a payment may be in various forms, such as in an image format.
Processing of the document may proceed wherein the data from the
document may be collected and lifted from the document as metadata.
This metadata is lifted from the document utilizing optical
character recognition (OCR). The OCR processes enables the system
to convert text and other symbols in the document image to
metadata, which can then be used and incorporated into exception
processing. In some embodiments, OCR based algorithms used in the
OCR processes incorporate pattern matching techniques. For example,
each character in an imaged word, phrase, code, or string of
alphanumeric text can be evaluated on a pixel-by-pixel basis and
matched to a stored character. Various algorithms may be repeatedly
applied to determine the best match between the image and stored
characters.
[0091] Once the metadata is lifted from the document associated
with a payment, as illustrated in block 602, the process 600
continues to compile and store the metadata associated with the
received financial documents, as illustrated in block 604. As such,
after the image of the document, such as a check, is processed, the
system may compile and store the lifted and collected check
information as metadata. As such, individual elements of the check
information may be stored separately, together, or the like.
[0092] Next, as illustrated in block 606, once the system receives
a document associated with a payment, the system matches the lifted
metadata from that document to the account to complete and process
the payment associated with that document.
[0093] In some embodiments, as illustrated in block 608, the system
may identify issues with the documents that suggest that the
documents should not be processed for payments. For example, the
documents may not match user provided data such as user account
information or the like.
[0094] In some embodiments, as illustrated in block 610, the system
may receive an indication from a user requesting a stop payment of
the financial document. The user indication may be a telephone
communication, text communication, electronic communication, or in
person communication. The user may provide the system via a
representative with one or more identifiers associated with the
document. For example, the user may wish to issue a stop payment on
a check he/she wrote. The user may only know one of the amount,
payee, date, or the like. The system may take this information from
the user, convert it to metadata and utilize that metadata to
search the financial institution to identify the specific document
and the account associated with the user's provided information. As
such, as illustrated in block 612 the system identifies the
financial document requested for stop payment based on the user
provided information. This is done by converting the user provided
information into metadata and searching the user provided data
against the metadata lifted from the document to identify a match.
In some embodiments, the user provided information is matched to
the lifted metadata associated with received financial documents
associated with payments from block 602.
[0095] Next, as illustrated in block 614 the system may stop
payment of the financial document in response to identifying the
document that was identified to not have a match to an account, as
illustrated in block 608, or the documents requested by a user to
issue a stop payment, as illustrated in block 612. Finally, the
process 600 continues to follow up with the financial institution
and user regarding the stopped payment financial document, as
illustrated in block 616. In this way, the system ensures that
there was a stop payment issued and no further action is
required.
[0096] FIG. 8 illustrates a process flow for metadata lift and
utilization for duplicate identification and processing 700, in
accordance with one embodiment of the present invention. As
illustrated in block 702, the process 700 is initiated when
financial documents are received. The received financial document
may be in various forms, such as in an image format. Processing of
the document may proceed wherein the data from the document may be
collected and lifted from the document as metadata. This metadata
is lifted from the document utilizing optical character recognition
(OCR). The OCR processes enables the system to convert text and
other symbols in the document image to metadata, which can then be
used and incorporated into exception processing. In some
embodiments, OCR based algorithms used in the OCR processes
incorporate pattern matching techniques. For example, each
character in an imaged word, phrase, code, or string of
alphanumeric text can be evaluated on a pixel-by-pixel basis and
matched to a stored character. Various algorithms may be repeatedly
applied to determine the best match between the image and stored
characters.
[0097] Once the metadata is lifted from the document as illustrated
in block 702, the process 700 continues to compile and store the
metadata associated with the received financial documents, as
illustrated in block 704. As such, after the image of the document
is processed, the system may compile and store the lifted and
collected information as metadata. As such, individual elements of
the document, such as a check, may be stored separately, together,
or the like. In this way, the system stores the type of document,
the appearance of the document, the information on the document,
such as numbers, accounts, dates, names, addresses, payee, payor,
routing numbers, amounts, document backgrounds, or the like as
metadata.
[0098] In some embodiments, the stored data may be structural
metadata. As such, the data may be about the design and
specification of the structure of the data. In other embodiments,
the data may be descriptive metadata. As such, the data may be data
describing in detail the content of the financial record or
document. In some embodiments, the metadata as described herein may
take the form of structural, descriptive and/or a combination
thereof.
[0099] Next, as illustrated in block 706, the metadata lifted from
the received documents is compared against metadata associated with
previously stored financial documents. In this way, the system may
identify if one or more received financial documents are duplicate
documents to those already stored within the system.
[0100] As illustrated in block 708, the system may identify a
duplicate financial documents based on the metadata comparison. As
such, the received document may be a duplicate of a document that
has already been received and is stored as metadata within the
financial institution. In some embodiments, the system may
eliminate the duplicate financial document. In other embodiments,
the system may notify the user of the duplicate financial document.
In yet other embodiments, the system may be both eliminate the
duplicate and notify the user of the duplicate. As such, as
illustrated in block 710 the user may be informed of the duplicate
and/or the identified duplicate may be eliminated and no longer
processed. Finally, as illustrated in block 712, the system may
follow on the duplicate document to ensure its elimination.
[0101] As will be appreciated by one of ordinary skill in the art,
the present invention may be embodied as an apparatus (including,
for example, a system, a machine, a device, a computer program
product, and/or the like), as a method (including, for example, a
business process, a computer-implemented process, and/or the like),
or as any combination of the foregoing. Accordingly, embodiments of
the present invention may take the form of an entirely software
embodiment (including firmware, resident software, micro-code, or
the like), an entirely hardware embodiment, or an embodiment
combining software and hardware aspects that may generally be
referred to herein as a "system." Furthermore, embodiments of the
present invention may take the form of a computer program product
that includes a computer-readable storage medium having
computer-executable program code portions stored therein. As used
herein, a processor may be "configured to" perform a certain
function in a verity of ways, including, for example, by having one
or more general-purpose circuits perform the functions by executing
one or more computer-executable program code portions embodied in a
computer-readable medium, and/or having one or more
application-specific circuits perform the function.
[0102] It will be understood that any suitable computer-readable
medium may be utilized. The computer-readable medium may include,
but is not limited to, a non-transitory computer-readable medium,
such as a tangible electronic, magnetic, optical, infrared,
electromagnetic, and/or semiconductor system, apparatus, and/or
device. For example, in some embodiments, the non-transitory
computer-readable medium includes a tangible medium such as a
portable computer diskette, a hard disk, a random access memory
(RAM), a read-only memory (ROM), an erasable programmable read-only
memory (EPROM or Flash memory), a compact disc read-only memory
(CD-ROM), and/or some other tangible optical and/or magnetic
storage device. In other embodiments of the present invention,
however, the computer-readable medium may be transitory, such as a
propagation signal including computer-executable program code
portions embodied therein.
[0103] It will also be understood that one or more
computer-executable program code portions for carrying out
operations of the present invention may include object-oriented,
scripted, and/or unscripted programming languages, such as, for
example, Java, Perl, Smalltalk, C++, SAS, SQL, Python, Objective C,
and/or the like. In some embodiments, the one or more
computer-executable program code portions for carrying out
operations of embodiments of the present invention are written in
conventional procedural programming languages, such as the "C"
programming languages and/or similar programming languages. The
computer program code may alternatively or additionally be written
in one or more multi-paradigm programming languages, such as, for
example, F#.
[0104] It will further be understood that some embodiments of the
present invention are described herein with reference to flowchart
illustrations and/or block diagrams of systems, methods, and/or
computer program products. It will be understood that each block
included in the flowchart illustrations and/or block diagrams, and
combinations of blocks included in the flowchart illustrations
and/or block diagrams, may be implemented by one or more
computer-executable program code portions. These one or more
computer-executable program code portions may be provided to a
processor of a general purpose computer, special purpose computer,
and/or some other programmable data processing apparatus in order
to produce a particular machine, such that the one or more
computer-executable program code portions, which execute via the
processor of the computer and/or other programmable data processing
apparatus, create mechanisms for implementing the steps and/or
functions represented by the flowchart(s) and/or block diagram
block(s).
[0105] It will also be understood that the one or more
computer-executable program code portions may be stored in a
transitory or non-transitory computer-readable medium (e.g., a
memory, or the like) that can direct a computer and/or other
programmable data processing apparatus to function in a particular
manner, such that the computer-executable program code portions
stored in the computer-readable medium produce an article of
manufacture including instruction mechanisms which implement the
steps and/or functions specified in the flowchart(s) and/or block
diagram block(s).
[0106] The one or more computer-executable program code portions
may also be loaded onto a computer and/or other programmable data
processing apparatus to cause a series of operational steps to be
performed on the computer and/or other programmable apparatus. In
some embodiments, this produces a computer-implemented process such
that the one or more computer-executable program code portions
which execute on the computer and/or other programmable apparatus
provide operational steps to implement the steps specified in the
flowchart(s) and/or the functions specified in the block diagram
block(s). Alternatively, computer-implemented steps may be combined
with operator and/or human-implemented steps in order to carry out
an embodiment of the present invention.
[0107] While certain exemplary embodiments have been described and
shown in the accompanying drawings, it is to be understood that
such embodiments are merely illustrative of, and not restrictive
on, the broad invention, and that this invention not be limited to
the specific constructions and arrangements shown and described,
since various other changes, combinations, omissions, modifications
and substitutions, in addition to those set forth in the above
paragraphs, are possible. Those skilled in the art will appreciate
that various adaptations and modifications of the just described
embodiments can be configured without departing from the scope and
spirit of the invention. Therefore, it is to be understood that,
within the scope of the appended claims, the invention may be
practiced other than as specifically described herein.
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